Unsupervised Learning of Representations from Solar Energy Data

In this paper, we propose an unsupervised method to learn hidden features of the solar energy generation from a PV system that may give a more accurate characterization of the process. In a first step, solar radiation data is converted into instantaneous solar power through a detailed source model. Then, two different approaches, namely PCA and autoencoder, are used to extract meaningful features from the traces of the solar energy generation. We interpret the latent variables characterizing the solar energy generation process by analyzing the similarities of 67 cities in Europe, North-Africa and Middle-East through an agglomerative hierarchical clustering algorithm. This analysis provides also a comparison between the feature extraction capabilities of the PCA and the autoencoder.